An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment.

With the advent of the digital information age, new data services such as virtual reality, industrial Internet, and cloud computing have proliferated in recent years. As a result, it increases operator demand for 5G bearer networks by providing features such as high transmission capacity, ultra-long transmission distance, network slicing, and intelligent management and control. Software-defined networking, as a new network architecture, intends to increase network flexibility and agility and can better satisfy the demands of 5G networks for network slicing. Nevertheless, software-defined networking still faces the challenge of network intrusion. We propose an abnormal traffic detection method based on the stacking method and self-attention mechanism, which makes up for the shortcoming of the inability to track long-term dependencies between data samples in ensemble learning. Our method utilizes a self-attention mechanism and a convolutional network to automatically learn long-term associations between traffic samples and provide them to downstream tasks in sample embedding. In addition, we design a novel stacking ensemble method, which computes the sample embedding and the predicted values of the heterogeneous base learner through the fusion module to obtain the final outlier results. This paper conducts experiments on abnormal traffic datasets in the software-defined network environment, calculates precision, recall and F1-score, and compares and analyzes them with other algorithms. The experimental results show that the method designed in this paper achieves 0.9972, 0.9996, and 0.9984 in multiple indicators of precision, recall, and F1-score, respectively, which are better than the comparison methods.

Proprotein convertase subtilisin/kexin type 9 inhibitor non responses in an adult with a history of coronary revascularization: A case report.

BACKGROUND: Familial hypercholesterolemia (FH) is an autosomal dominant disorder that is characterized by severely increased low-density lipoprotein (LDL) cholesterol levels. At the same time, elevated LDL levels accelerated the development of coronary heart disease. Several classes of drugs are currently in use to treat FH. Proprotein convertase subtilisin/kexin type 9 inhibitor (PCSK9i) is novel one of these. CASE SUMMARY: This manuscript reports a case of FH that responded modestly after treatment with PCSK9i and statin drugs. Of even more concern is that the patient frequently admitted to the hospital during a 12-year follow-up period. Subsequently, we identified a heterozygous mutation, 1448G>A (W483X) of the LDL receptor (LDLR) in this patient. The serum levels of PCSK9 (proprotein convertase subtilisin/kexin type 9) in the patient was 71.30 ± 26.66 ng/mL, which is close the average level reported in the literature. This LDLR mutation affects LDLR metabolism or structure, which may make it unsuitable for use of PCSK9i. CONCLUSION: Our outcome demonstrates that LDLR-W483X represents a partial loss-of-function LDLR and may contribute to PCSK9i ineffective. In the meanwhile, additional measures are therefore required (particularly with gene sequencing or change the treatment plan) must be initiated as early as possible. Genetic testing for clinically challenging cases who do not respond to PCSK9i therapy is very helpful.